Neural network based line of sight detection for positioning

ABSTRACT

Techniques are provide for neural network based positioning of a mobile device. An example method for determining a line of sight delay, an angle of arrival, or an angle of departure value, according to the disclosure includes receiving reference signal information, determining a channel frequency response or a channel impulse response based on the reference signal information, processing the channel frequency response or the channel impulse response with a neural network, and determining the line of sight delay, the angle of arrival, or the angle of departure value based on an output of the neural network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/009,571, filed Apr. 14, 2020, entitled “Neural Network Based Line ofSight Detection for Positioning,” which is assigned to the assigneehereof, and the entire contents of which are hereby incorporated hereinby reference for all purposes.

BACKGROUND

Wireless communication systems have developed through variousgenerations, including a first-generation analog wireless phone service(1G), a second-generation (2G) digital wireless phone service (includinginterim 2.5G and 2.75G networks), a third-generation (3G) high speeddata, Internet-capable wireless service, a fourth-generation (4G)service (e.g., Long Term Evolution (LTE) or WiMax), and afifth-generation (5G) service (e.g., 5G New Radio (NR)). There arepresently many different types of wireless communication systems in use,including Cellular and Personal Communications Service (PCS) systems.Examples of known cellular systems include the cellular Analog AdvancedMobile Phone System (AMPS), and digital cellular systems based on CodeDivision Multiple Access (CDMA), Frequency Division Multiple Access(FDMA), Time Division Multiple Access (TDMA), the Global System forMobile access (GSM) variation of TDMA, etc.

It is often desirable to know the location of a user equipment (UE),e.g., a cellular phone, with the terms “location” and “position” beingsynonymous and used interchangeably herein. A location services (LCS)client may desire to know the location of the UE and may communicatewith a location center in order to request the location of the UE. Thelocation center and the UE may exchange messages, as appropriate, toobtain a location estimate for the UE. The location center may returnthe location estimate to the LCS client, e.g., for use in one or moreapplications.

Obtaining the location of a mobile device that is accessing a wirelessnetwork may be useful for many applications including, for example,emergency calls, personal navigation, asset tracking, locating a friendor family member, etc. Existing positioning methods include methodsbased on measuring radio signals transmitted from a variety of devicesincluding satellite vehicles and terrestrial radio sources in a wirelessnetwork such as base stations and access points. 5G networks, forexample, will be deployed with larger bandwidths (BW), use higherfrequencies such as millimeter wave (mmW) spectrum, have densertopologies and will use large antenna arrays enabling directionaltransmissions. These 5G networks are designed for both outdoor andindoor deployments and may support deployment by private entities otherthan cellular operators. Such network deployments are expected toprovide high precision positioning based services.

SUMMARY

An example method for determining a neural network to provide a line ofsight delay estimate according to the disclosure includes determiningreceiver configuration information or dynamic channel state informationfor a mobile device, determining neural network information based on thereceiver configuration information or the channel state information, andproviding the neural network information to the mobile device.

Implementations of such a method may include one or more of thefollowing features. The receiver configuration information may includean antenna configuration. The antenna configuration may include a phasecoherence state of the antenna configuration. The channel stateinformation may include a power delay profile. The receiverconfiguration information may include an operating frequency andbandwidth. The neural network information may be stored on a networkserver. The neural network information may be stored on the mobiledevice. The neural network information may include an architecture ofthe neural network and its weight and bias matrices. The weight and biasvalues may be truncated to reduce a complexity of the neural networkinformation.

An example method for determining a line of sight delay, an angle ofarrival, or an angle of departure value, according to the disclosureincludes receiving reference signal information, determining a channelfrequency response or a channel impulse response based on the referencesignal information, processing the channel frequency response or thechannel impulse response with a neural network, and determining the lineof sight delay, the angle of arrival, or the angle of departure valuebased on an output of the neural network.

Implementations of such a method may include one or more of thefollowing features. The reference signal information may be a soundingreference signal. The reference signal information may be a positioningreference signal. The reference signal information may be a channelstate information reference signal. The neural network may be determinedbased at least in part of a positioning method used for determining alocation of a mobile device. Determining the neural network may be basedat least in part on a receiver configuration. The receiver configurationmay include an antenna configuration and a phase coherence state of theantenna configuration. Determining the neural network may includetransmitting the neural network information from a network to a mobiledevice. Determining the neural network may include transmitting anindication of a selected neural network from a list of neural networksavailable at a mobile device. The neural network may be one of aplurality of neural networks stored in a data structure. A requireddesired accuracy associated with the output of the neural network may bedetermined, and one or more weights in the neural network may be adaptedbased on the required desired accuracy. The output of the neural networkincludes a quality estimate. Determining the line of sight delay, theangle of arrival, or the angle of departure value may be based at leastin part on the quality estimate.

An example apparatus for determining a neural network to provide a lineof sight delay estimate according to the disclosure includes a memory,at least one transceiver, at least one processor operably coupled to thememory and the at least one transceiver, and configured to determinereceiver configuration information or dynamic channel state informationfor a mobile device, determine neural network information based on thereceiver configuration information or the channel state information, andprovide the neural network information to the mobile device.

Implementations of such an apparatus may include one or more of thefollowing features. The receiver configuration information may includean antenna configuration. The antenna configuration may include a phasecoherence state of the antenna configuration. The channel stateinformation may include a power delay profile. The receiverconfiguration information may include an operating frequency andbandwidth. The neural network information may be stored on a networkserver. The neural network information may be stored on the mobiledevice. The neural network information may include an architecture ofthe neural network and its weight and bias matrices. The at least oneprocessor may be further configured to truncate the weight and biasvalues to reduce a complexity of the neural network information.

An example apparatus for determining a line of sight delay, an angle ofarrival, or an angle of departure value, according to the disclosureincludes a memory, at least one processor operably coupled to the memoryand configured to receive reference signal information, determine achannel frequency response or a channel impulse response based on thereference signal information, process the channel frequency response orthe channel impulse response with a neural network, and determine theline of sight delay, the angle of arrival, or the angle of departurevalue based on an output of the neural network.

Implementations of such an apparatus may include one or more of thefollowing features. The reference signal information may be a soundingreference signal. The reference signal information may be a positioningreference signal. The reference signal information may be a channelstate information reference signal. The at least one processor may befurther configured to determine the neural network based at least inpart of a positioning method used to determine a location of a mobiledevice. The at least one processor may be further configured todetermine the neural network based at least in part on a receiverconfiguration. The receiver configuration may include an antennaconfiguration and a phase coherence state of the antenna configuration.The apparatus may include at least one transceiver operably coupled tothe memory and the at least one processor, such that the at least oneprocessor may be further configured to transmit the neural networkinformation from a network to a mobile device. The at least oneprocessor may be further configured to transmit an indication of aselected neural network from a list of neural networks available at amobile device. The neural network may be one of a plurality of neuralnetworks stored in a data structure. The at least one processor may befurther configured to determine a required desired accuracy associatedwith the line of sight delay and adapt one or more weights in the neuralnetwork based on the required desired accuracy. The output of the neuralnetwork may include a quality estimate. The at least one processor maybe further configured to determine the line of sight delay, the angle ofarrival, or the angle of departure value based at least in part on thequality estimate.

An example apparatus for determining a neural network to provide a lineof sight delay estimate according to the disclosure includes means fordetermining receiver configuration information or dynamic channel stateinformation for a mobile device, means for determining neural networkinformation based on the receiver configuration information or thechannel state information, and means for providing the neural networkinformation to the mobile device.

An example apparatus for determining a line of sight delay, an angle ofarrival, or an angle of departure value according to the disclosureincludes means for receiving reference signal information, means fordetermining a channel frequency response or a channel impulse responsebased on the reference signal information, means for processing thechannel frequency response or the channel impulse response with a neuralnetwork, and means for determining the line of sight delay, the angle ofarrival, or the angle of departure value based on an output of theneural network.

An example non-transitory processor-readable storage medium comprisingprocessor-readable instructions configured to cause one or moreprocessors to determine a neural network to provide a line of sightdelay estimate according to the disclosure includes code for determiningreceiver configuration information or dynamic channel state informationfor a mobile device, code for determining neural network informationbased on the receiver configuration information or the channel stateinformation, and code for providing the neural network information tothe mobile device.

An example non-transitory processor-readable storage medium comprisingprocessor-readable instructions configured to cause one or moreprocessors to determine a line of sight delay, an angle of arrival, oran angle of departure value according to the disclosure includes codefor receiving reference signal information, code for determining achannel frequency response or a channel impulse response based on thereference signal information, code for processing the channel frequencyresponse or the channel impulse response with a neural network, and codefor determining the line of sight delay, the angle of arrival, or theangle of departure value based on an output of the neural network.

Items and/or techniques described herein may provide one or more of thefollowing capabilities, as well as other capabilities not mentioned. Theconfiguration of a receive chain may be determined. The configurationmay include an antenna element configuration and phase coherence state.A neural network may be selected based on the configuration information.A channel impulse response may be input into the neural network. A lineof sight delay estimate may be output from the neural network. The lineof sight delay estimate may be used in positioning methods. Othercapabilities may be provided and not every implementation according tothe disclosure must provide any, let alone all, of the capabilitiesdiscussed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified diagram of an example wireless communicationssystem.

FIG. 2 is a block diagram of components of an example user equipmentshown in FIG. 1 .

FIG. 3 is a block diagram of components of an exampletransmission/reception point shown in FIG. 1 .

FIG. 4 is a block diagram of components of an example server shown inFIG. 1 .

FIG. 5 is a conceptual diagram of an example line of sight between abase station and a mobile device.

FIG. 6 is a conceptual diagram of an example position determinationbased on a line of sight signal.

FIG. 7 is a flow diagram of an example process for generating a channelimpulse response input for a neural network.

FIG. 8 is a block diagram of an example neural network for determining aline of sight delay estimate.

FIG. 9A is a block diagram of an example pointwise convolution layer ina neural network.

FIG. 9B is a block diagram of an example depth wise convolution layer ina neural network.

FIG. 10 includes example message flows between a base station and amobile device for determining neural network information.

FIG. 11 includes example message flows between a base station and amobile device for retraining a neural network.

FIG. 12 is an example data structure for neural network models.

FIG. 13A is a process flow diagram for an example method for providingneural network information to a mobile device.

FIG. 13B is a process flow diagram for an example method for computing aline of sight delay based on neural network information.

FIG. 14 is a process flow diagram of an example method for determining aline of sight delay.

DETAILED DESCRIPTION

Techniques are discussed herein for neural network based positioning ofa mobile device. For example, the disclosure addresses the problem ofaccurate line-of-sight (LOS) delay estimation in a wireless channelusing deep neural networks (NN), which can be used as a building blockto derive accurate position estimates. A NN may be used to exploit theproperties of the wireless channel to estimate a LOS delay. The proposedNN shows improved performance in the presence of weak LOS signals anddense multipath, which are typically challenging scenarios fortraditional signal processing algorithms. These techniques andconfigurations are examples, and other techniques and configurations maybe used.

In general, positioning methods may be classified into two categories:(1) Geometric/parametric methods including intermediate parameters suchas time of arrival (ToA), time difference of arrival (TDoA), angle ofarrival & departure (AoA/AoD), round trip time (RTT) are first computedand then input to a measurement model to derive the final locationestimate, and (2) Non-parametric methods which learn the “similarity”between the measurements at known locations and use this information topredict the location given a new set of measurements. The methodsdisclosed herein compute intermediate parameters for positioning,specifically estimating the LOS delay of a signal arriving from atransmitter to a receiver, which translates to a distance estimatebetween the two devices. The term ‘LOS delay’ as used herein is ageneric term to also imply the first arriving path of the channel, whichin some scenarios may not be the physical line of sight path.

Referring to FIG. 1 , an example of a communication system 100 includesa UE 105, a Radio Access Network (RAN) 135, here a Fifth Generation (5G)Next Generation (NG) RAN (NG-RAN), and a 5G Core Network (5GC) 140. TheUE 105 may be, e.g., an IoT device, a location tracker device, acellular telephone, or other device. A 5G network may also be referredto as a New Radio (NR) network; NG-RAN 135 may be referred to as a 5GRAN or as an NR RAN; and 5GC 140 may be referred to as an NG Corenetwork (NGC). Standardization of an NG-RAN and 5GC is ongoing in the3^(rd) Generation Partnership Project (3GPP). Accordingly, the NG-RAN135 and the 5GC 140 may conform to current or future standards for 5Gsupport from 3GPP. The RAN 135 may be another type of RAN, e.g., a 3GRAN, a 4G Long Term Evolution (LTE) RAN, etc. The communication system100 may utilize information from a constellation 185 of satellitevehicles (SVs) 190, 191, 192, 193 for a Satellite Positioning System(SPS) (e.g., a Global Navigation Satellite System (GNSS)) like theGlobal Positioning System (GPS), the Global Navigation Satellite System(GLONASS), Galileo, or Beidou or some other local or regional SPS suchas the Indian Regional Navigational Satellite System (IRNSS), theEuropean Geostationary Navigation Overlay Service (EGNOS), or the WideArea Augmentation System (WAAS). Additional components of thecommunication system 100 are described below. The communication system100 may include additional or alternative components.

As shown in FIG. 1 , the NG-RAN 135 includes NR nodeBs (gNBs) 110 a, 110b, and a next generation eNodeB (ng-eNB) 114, and the 5GC 140 includesan Access and Mobility Management Function (AMF) 115, a SessionManagement Function (SMF) 117, a Location Management Function (LMF) 120,and a Gateway Mobile Location Center (GMLC) 125. The gNBs 110 a, 110 band the ng-eNB 114 are communicatively coupled to each other, are eachconfigured to bi-directionally wirelessly communicate with the UE 105,and are each communicatively coupled to, and configured tobi-directionally communicate with, the AMF 115. The AMF 115, the SMF117, the LMF 120, and the GMLC 125 are communicatively coupled to eachother, and the GMLC is communicatively coupled to an external client130. The SMF 117 may serve as an initial contact point of a ServiceControl Function (SCF) (not shown) to create, control, and delete mediasessions.

FIG. 1 provides a generalized illustration of various components, any orall of which may be utilized as appropriate, and each of which may beduplicated or omitted as necessary. Specifically, although only one UE105 is illustrated, many UEs (e.g., hundreds, thousands, millions, etc.)may be utilized in the communication system 100. Similarly, thecommunication system 100 may include a larger (or smaller) number of SVs(i.e., more or fewer than the four SVs 190-193 shown), gNBs 110 a, 110b, ng-eNBs 114, AMFs 115, external clients 130, and/or other components.The illustrated connections that connect the various components in thecommunication system 100 include data and signaling connections whichmay include additional (intermediary) components, direct or indirectphysical and/or wireless connections, and/or additional networks.Furthermore, components may be rearranged, combined, separated,substituted, and/or omitted, depending on desired functionality.

While FIG. 1 illustrates a 5G-based network, similar networkimplementations and configurations may be used for other communicationtechnologies, such as 3G, Long Term Evolution (LTE), etc.Implementations described herein (be they for 5G technology and/or forone or more other communication technologies and/or protocols) may beused to transmit (or broadcast) directional synchronization signals,receive and measure directional signals at UEs (e.g., the UE 105) and/orprovide location assistance to the UE 105 (via the GMLC 125 or otherlocation server) and/or compute a location for the UE 105 at alocation-capable device such as the UE 105, the gNB 110 a, 110 b, or theLMF 120 based on measurement quantities received at the UE 105 for suchdirectionally-transmitted signals. The gateway mobile location center(GMLC) 125, the location management function (LMF) 120, the access andmobility management function (AMF) 115, the SMF 117, the ng-eNB (eNodeB)114 and the gNBs (gNodeBs) 110 a, 110 b are examples and may, in variousembodiments, be replaced by or include various other location serverfunctionality and/or base station functionality respectively.

The UE 105 may comprise and/or may be referred to as a device, a mobiledevice, a wireless device, a mobile terminal, a terminal, a mobilestation (MS), a Secure User Plane Location (SUPL) Enabled Terminal(SET), or by some other name. Moreover, the UE 105 may correspond to acellphone, smartphone, laptop, tablet, PDA, tracking device, navigationdevice, Internet of Things (IoT) device, asset tracker, health monitors,security systems, smart city sensors, smart meters, wearable trackers,or some other portable or moveable device. Typically, though notnecessarily, the UE 105 may support wireless communication using one ormore Radio Access Technologies (RATs) such as Global System for Mobilecommunication (GSM), Code Division Multiple Access (CDMA), Wideband CDMA(WCDMA), LTE, High Rate Packet Data (HRPD), IEEE 802.11 WiFi (alsoreferred to as Wi-Fi), Bluetooth® (BT), Worldwide Interoperability forMicrowave Access (WiMAX), 5G new radio (NR) (e.g., using the NG-RAN 135and the 5GC 140), etc. The UE 105 may support wireless communicationusing a Wireless Local Area Network (WLAN) which may connect to othernetworks (e.g., the Internet) using a Digital Subscriber Line (DSL) orpacket cable, for example. The use of one or more of these RATs mayallow the UE 105 to communicate with the external client 130 (e.g., viaelements of the 5GC 140 not shown in FIG. 1 , or possibly via the GMLC125) and/or allow the external client 130 to receive locationinformation regarding the UE 105 (e.g., via the GMLC 125).

The UE 105 may include a single entity or may include multiple entitiessuch as in a personal area network where a user may employ audio, videoand/or data I/O (input/output) devices and/or body sensors and aseparate wireline or wireless modem. An estimate of a location of the UE105 may be referred to as a location, location estimate, location fix,fix, position, position estimate, or position fix, and may begeographic, thus providing location coordinates for the UE 105 (e.g.,latitude and longitude) which may or may not include an altitudecomponent (e.g., height above sea level, height above or depth belowground level, floor level, or basement level). Alternatively, a locationof the UE 105 may be expressed as a civic location (e.g., as a postaladdress or the designation of some point or small area in a buildingsuch as a particular room or floor). A location of the UE 105 may beexpressed as an area or volume (defined either geographically or incivic form) within which the UE 105 is expected to be located with someprobability or confidence level (e.g., 67%, 95%, etc.). A location ofthe UE 105 may be expressed as a relative location comprising, forexample, a distance and direction from a known location. The relativelocation may be expressed as relative coordinates (e.g., X, Y (and Z)coordinates) defined relative to some origin at a known location whichmay be defined, e.g., geographically, in civic terms, or by reference toa point, area, or volume, e.g., indicated on a map, floor plan, orbuilding plan. In the description contained herein, the use of the termlocation may comprise any of these variants unless indicated otherwise.When computing the location of a UE, it is common to solve for local x,y, and possibly z coordinates and then, if desired, convert the localcoordinates into absolute coordinates (e.g., for latitude, longitude,and altitude above or below mean sea level).

The UE 105 may be configured to communicate with other entities usingone or more of a variety of technologies. The UE 105 may be configuredto connect indirectly to one or more communication networks via one ormore device-to-device (D2D) peer-to-peer (P2P) links. The D2D P2P linksmay be supported with any appropriate D2D radio access technology (RAT),such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), Bluetooth®, and so on.One or more of a group of UEs utilizing D2D communications may be withina geographic coverage area of a Transmission/Reception Point (TRP) suchas one or more of the gNBs 110 a, 110 b, and/or the ng-eNB 114. OtherUEs in such a group may be outside such geographic coverage areas, ormay be otherwise unable to receive transmissions from a base station.Groups of UEs communicating via D2D communications may utilize aone-to-many (1:M) system in which each UE may transmit to other UEs inthe group. A TRP may facilitate scheduling of resources for D2Dcommunications. In other cases, D2D communications may be carried outbetween UEs without the involvement of a TRP.

Base stations (BSs) in the NG-RAN 135 shown in FIG. 1 include NR NodeBs, referred to as the gNBs 110 a and 110 b. Pairs of the gNBs 110 a,110 b in the NG-RAN 135 may be connected to one another via one or moreother gNBs. Access to the 5G network is provided to the UE 105 viawireless communication between the UE 105 and one or more of the gNBs110 a, 110 b, which may provide wireless communications access to the5GC 140 on behalf of the UE 105 using 5G. In FIG. 1 , the serving gNBfor the UE 105 is assumed to be the gNB 110 a, although another gNB(e.g. the gNB 110 b) may act as a serving gNB if the UE 105 moves toanother location or may act as a secondary gNB to provide additionalthroughput and bandwidth to the UE 105.

Base stations (BSs) in the NG-RAN 135 shown in FIG. 1 may include theng-eNB 114, also referred to as a next generation evolved Node B. Theng-eNB 114 may be connected to one or more of the gNBs 110 a, 110 b inthe NG-RAN 135, possibly via one or more other gNBs and/or one or moreother ng-eNBs. The ng-eNB 114 may provide LTE wireless access and/orevolved LTE (eLTE) wireless access to the UE 105. One or more of thegNBs 110 a, 110 b and/or the ng-eNB 114 may be configured to function aspositioning-only beacons which may transmit signals to assist withdetermining the position of the UE 105 but may not receive signals fromthe UE 105 or from other UEs.

The BSs 110 a, 110 b, 114 may each comprise one or more TRPs. Forexample, each sector within a cell of a BS may comprise a TRP, althoughmultiple TRPs may share one or more components (e.g., share a processorbut have separate antennas). The system 100 may include only macro TRPsor the system 100 may have TRPs of different types, e.g., macro, pico,and/or femto TRPs, etc. A macro TRP may cover a relatively largegeographic area (e.g., several kilometers in radius) and may allowunrestricted access by terminals with service subscription. A pico TRPmay cover a relatively small geographic area (e.g., a pico cell) and mayallow unrestricted access by terminals with service subscription. Afemto or home TRP may cover a relatively small geographic area (e.g., afemto cell) and may allow restricted access by terminals havingassociation with the femto cell (e.g., terminals for users in a home).

As noted, while FIG. 1 depicts nodes configured to communicate accordingto 5G communication protocols, nodes configured to communicate accordingto other communication protocols, such as, for example, an LTE protocolor IEEE 802.11x protocol, may be used. For example, in an Evolved PacketSystem (EPS) providing LTE wireless access to the UE 105, a RAN maycomprise an Evolved Universal Mobile Telecommunications System (UMTS)Terrestrial Radio Access Network (E-UTRAN) which may comprise basestations comprising evolved Node Bs (eNBs). A core network for EPS maycomprise an Evolved Packet Core (EPC). An EPS may comprise an E-UTRANplus EPC, where the E-UTRAN corresponds to the NG-RAN 135 and the EPCcorresponds to the 5GC 140 in FIG. 1 .

The gNBs 110 a, 110 b and the ng-eNB 114 may communicate with the AMF115, which, for positioning functionality, communicates with the LMF120. The AMF 115 may support mobility of the UE 105, including cellchange and handover and may participate in supporting a signalingconnection to the UE 105 and possibly data and voice bearers for the UE105. The LMF 120 may communicate directly with the UE 105, e.g., throughwireless communications. The LMF 120 may support positioning of the UE105 when the UE 105 accesses the NG-RAN 135 and may support positionprocedures/methods such as Assisted GNSS (A-GNSS), Observed TimeDifference of Arrival (OTDOA), Real Time Kinematics (RTK), Precise PointPositioning (PPP), Differential GNSS (DGNSS), Enhanced Cell ID (E-CID),angle of arrival (AOA), angle of departure (AOD), and/or other positionmethods. The LMF 120 may process location services requests for the UE105, e.g., received from the AMF 115 or from the GMLC 125. The LMF 120may be connected to the AMF 115 and/or to the GMLC 125. The LMF 120 maybe referred to by other names such as a Location Manager (LM), LocationFunction (LF), commercial LMF (CLMF), or value added LMF (VLMF). Anode/system that implements the LMF 120 may additionally oralternatively implement other types of location-support modules, such asan Enhanced Serving Mobile Location Center (E-SMLC) or a Secure UserPlane Location (SUPL) Location Platform (SLP). At least part of thepositioning functionality (including derivation of the location of theUE 105) may be performed at the UE 105 (e.g., using signal measurementsobtained by the UE 105 for signals transmitted by wireless nodes such asthe gNBs 110 a, 110 b and/or the ng-eNB 114, and/or assistance dataprovided to the UE 105, e.g. by the LMF 120).

The GMLC 125 may support a location request for the UE 105 received fromthe external client 130 and may forward such a location request to theAMF 115 for forwarding by the AMF 115 to the LMF 120 or may forward thelocation request directly to the LMF 120. A location response from theLMF 120 (e.g., containing a location estimate for the UE 105) may bereturned to the GMLC 125 either directly or via the AMF 115 and the GMLC125 may then return the location response (e.g., containing the locationestimate) to the external client 130. The GMLC 125 is shown connected toboth the AMF 115 and LMF 120, though only one of these connections maybe supported by the 5GC 140 in some implementations.

As further illustrated in FIG. 1 , the LMF 120 may communicate with thegNBs 110 a, 110 b and/or the ng-eNB 114 using a New Radio PositionProtocol A (which may be referred to as NPPa or NRPPa), which may bedefined in 3GPP Technical Specification (TS) 38.455. NRPPa may be thesame as, similar to, or an extension of the LTE Positioning Protocol A(LPPa) defined in 3GPP TS 36.455, with NRPPa messages being transferredbetween the gNB 110 a (or the gNB 110 b) and the LMF 120, and/or betweenthe ng-eNB 114 and the LMF 120, via the AMF 115. As further illustratedin FIG. 1 , the LMF 120 and the UE 105 may communicate using an LTEPositioning Protocol (LPP), which may be defined in 3GPP TS 36.355. TheLMF 120 and the UE 105 may also or instead communicate using a New RadioPositioning Protocol (which may be referred to as NPP or NRPP), whichmay be the same as, similar to, or an extension of LPP. Here, LPP and/orNPP messages may be transferred between the UE 105 and the LMF 120 viathe AMF 115 and the serving gNB 110 a, 110 b or the serving ng-eNB 114for the UE 105. For example, LPP and/or NPP messages may be transferredbetween the LMF 120 and the AMF 115 using a 5G Location ServicesApplication Protocol (LCS AP) and may be transferred between the AMF 115and the UE 105 using a 5G Non-Access Stratum (NAS) protocol. The LPPand/or NPP protocol may be used to support positioning of the UE 105using UE-assisted and/or UE-based position methods such as A-GNSS, RTK,OTDOA and/or E-CID. The NRPPa protocol may be used to supportpositioning of the UE 105 using network-based position methods such asE-CID (e.g., when used with measurements obtained by the gNB 110 a, 110b or the ng-eNB 114) and/or may be used by the LMF 120 to obtainlocation related information from the gNBs 110 a, 110 b and/or theng-eNB 114, such as parameters defining directional SS transmissionsfrom the gNBs 110 a, 110 b, and/or the ng-eNB 114.

With a UE-assisted position method, the UE 105 may obtain locationmeasurements and send the measurements to a location server (e.g., theLMF 120) for computation of a location estimate for the UE 105. Forexample, the location measurements may include one or more of a ReceivedSignal Strength Indication (RSSI), Round Trip signal propagation Time(RTT), Reference Signal Time Difference (RSTD), Reference SignalReceived Power (RSRP) and/or Reference Signal Received Quality (RSRQ)for the gNBs 110 a, 110 b, the ng-eNB 114, and/or a WLAN AP. Thelocation measurements may also or instead include measurements of GNSSpseudorange, code phase, and/or carrier phase for the SVs 190-193.

With a UE-based position method, the UE 105 may obtain locationmeasurements (e.g., which may be the same as or similar to locationmeasurements for a UE-assisted position method) and may compute alocation of the UE 105 (e.g., with the help of assistance data receivedfrom a location server such as the LMF 120 or broadcast by the gNBs 110a, 110 b, the ng-eNB 114, or other base stations or APs).

With a network-based position method, one or more base stations (e.g.,the gNBs 110 a, 110 b, and/or the ng-eNB 114) or APs may obtain locationmeasurements (e.g., measurements of RSSI, RTT, RSRP, RSRQ or Time OfArrival (TOA) for signals transmitted by the UE 105) and/or may receivemeasurements obtained by the UE 105. The one or more base stations orAPs may send the measurements to a location server (e.g., the LMF 120)for computation of a location estimate for the UE 105.

Information provided by the gNBs 110 a, 110 b, and/or the ng-eNB 114 tothe LMF 120 using NRPPa may include timing and configuration informationfor directional SS transmissions and location coordinates. The LMF 120may provide some or all of this information to the UE 105 as assistancedata in an LPP and/or NPP message via the NG-RAN 135 and the 5GC 140.

An LPP or NPP message sent from the LMF 120 to the UE 105 may instructthe UE 105 to do any of a variety of things depending on desiredfunctionality. For example, the LPP or NPP message could contain aninstruction for the UE 105 to obtain measurements for GNSS (or A-GNSS),WLAN, E-CID, and/or OTDOA (or some other position method). In the caseof E-CID, the LPP or NPP message may instruct the UE 105 to obtain oneor more measurement quantities (e.g., beam ID, beam width, mean angle,RSRP, RSRQ measurements) of directional signals transmitted withinparticular cells supported by one or more of the gNBs 110 a, 110 b,and/or the ng-eNB 114 (or supported by some other type of base stationsuch as an eNB or WiFi AP). The UE 105 may send the measurementquantities back to the LMF 120 in an LPP or NPP message (e.g., inside a5G NAS message) via the serving gNB 110 a (or the serving ng-eNB 114)and the AMF 115.

As noted, while the communication system 100 is described in relation to5G technology, the communication system 100 may be implemented tosupport other communication technologies, such as GSM, WCDMA, LTE, etc.,that are used for supporting and interacting with mobile devices such asthe UE 105 (e.g., to implement voice, data, positioning, and otherfunctionalities). In some such embodiments, the 5GC 140 may beconfigured to control different air interfaces. For example, the 5GC 140may be connected to a WLAN using a Non-3GPP InterWorking Function(N3IWF, not shown FIG. 1 ) in the 5GC 150. For example, the WLAN maysupport IEEE 802.11 WiFi access for the UE 105 and may comprise one ormore WiFi APs. Here, the N3IWF may connect to the WLAN and to otherelements in the 5GC 140 such as the AMF 115. In some embodiments, boththe NG-RAN 135 and the 5GC 140 may be replaced by one or more other RANsand one or more other core networks. For example, in an EPS, the NG-RAN135 may be replaced by an E-UTRAN containing eNBs and the 5GC 140 may bereplaced by an EPC containing a Mobility Management Entity (MME) inplace of the AMF 115, an E-SMLC in place of the LMF 120, and a GMLC thatmay be similar to the GMLC 125. In such an EPS, the E-SMLC may use LPPain place of NRPPa to send and receive location information to and fromthe eNBs in the E-UTRAN and may use LPP to support positioning of the UE105. In these other embodiments, positioning of the UE 105 usingdirectional PRSs may be supported in an analogous manner to thatdescribed herein for a 5G network with the difference that functions andprocedures described herein for the gNBs 110 a, 110 b, the ng-eNB 114,the AMF 115, and the LMF 120 may, in some cases, apply instead to othernetwork elements such eNBs, WiFi APs, an MME, and an E-SMLC.

As noted, in some embodiments, positioning functionality may beimplemented, at least in part, using the directional SS beams, sent bybase stations (such as the gNBs 110 a, 110 b, and/or the ng-eNB 114)that are within range of the UE whose position is to be determined(e.g., the UE 105 of FIG. 1 ). The UE may, in some instances, use thedirectional SS beams from a plurality of base stations (such as the gNBs110 a, 110 b, the ng-eNB 114, etc.) to compute the UE's position.

Referring also to FIG. 2 , a UE 200 is an example of the UE 105 andcomprises a computing platform including a processor 210, memory 211including software (SW) 212, one or more sensors 213, a transceiverinterface 214 for a transceiver 215, a user interface 216, a SatellitePositioning System (SPS) receiver 217, a camera 218, and a position(motion) device 219. The processor 210, the memory 211, the sensor(s)213, the transceiver interface 214, the user interface 216, the SPSreceiver 217, the camera 218, and the position (motion) device 219 maybe communicatively coupled to each other by a bus 220 (which may beconfigured, e.g., for optical and/or electrical communication). One ormore of the shown apparatus (e.g., the camera 218, the position (motion)device 219, and/or one or more of the sensor(s) 213, etc.) may beomitted from the UE 200. The processor 210 may include one or moreintelligent hardware devices, e.g., a central processing unit (CPU), amicrocontroller, an application specific integrated circuit (ASIC), etc.The processor 210 may comprise multiple processors including ageneral-purpose/application processor 230, a Digital Signal Processor(DSP) 231, a modem processor 232, a video processor 233, and/or a sensorprocessor 234. One or more of the processors 230-234 may comprisemultiple devices (e.g., multiple processors). For example, the sensorprocessor 234 may comprise, e.g., processors for radar, ultrasound,and/or lidar, etc. The modem processor 232 may support dual SIM/dualconnectivity (or even more SIMs). For example, a SIM (SubscriberIdentity Module or Subscriber Identification Module) may be used by anOriginal Equipment Manufacturer (OEM), and another SIM may be used by anend user of the UE 200 for connectivity. The memory 211 is anon-transitory storage medium that may include random access memory(RAM), flash memory, disc memory, and/or read-only memory (ROM), etc.The memory 211 stores the software 212 which may be processor-readable,processor-executable software code containing instructions that areconfigured to, when executed, cause the processor 210 to perform variousfunctions described herein. Alternatively, the software 212 may not bedirectly executable by the processor 210 but may be configured to causethe processor 210, e.g., when compiled and executed, to perform thefunctions. The description may refer only to the processor 210performing a function, but this includes other implementations such aswhere the processor 210 executes software and/or firmware. Thedescription may refer to the processor 210 performing a function asshorthand for one or more of the processors 230-234 performing thefunction. The description may refer to the UE 200 performing a functionas shorthand for one or more appropriate components of the UE 200performing the function. The processor 210 may include a memory withstored instructions in addition to and/or instead of the memory 211.Functionality of the processor 210 is discussed more fully below.

The configuration of the UE 200 shown in FIG. 2 is an example and notlimiting of the disclosure, including the claims, and otherconfigurations may be used. For example, an example configuration of theUE includes one or more of the processors 230-234 of the processor 210,the memory 211, and the wireless transceiver 240. Other exampleconfigurations include one or more of the processors 230-234 of theprocessor 210, the memory 211, the wireless transceiver 240, and one ormore of the sensor(s) 213, the user interface 216, the SPS receiver 217,the camera 218, the PMD 219, and/or the wired transceiver 250.

The UE 200 may comprise the modem processor 232 that may be capable ofperforming baseband processing of signals received and down-converted bythe transceiver 215 and/or the SPS receiver 217. The modem processor 232may perform baseband processing of signals to be upconverted fortransmission by the transceiver 215. Also or alternatively, basebandprocessing may be performed by the processor 230 and/or the DSP 231.Other configurations, however, may be used to perform basebandprocessing.

The UE 200 may include the sensor(s) 213 that may include, for example,an Inertial Measurement Unit (IMU) 270, one or more magnetometers 271,and/or one or more environment sensors 272. The IMU 270 may comprise oneor more inertial sensors, for example, one or more accelerometers 273(e.g., collectively responding to acceleration of the UE 200 in threedimensions) and/or one or more gyroscopes 274. The magnetometer(s) mayprovide measurements to determine orientation (e.g., relative tomagnetic north and/or true north) that may be used for any of a varietyof purposes, e.g., to support one or more compass applications. Theenvironment sensor(s) 272 may comprise, for example, one or moretemperature sensors, one or more barometric pressure sensors, one ormore ambient light sensors, one or more camera imagers, and/or one ormore microphones, etc. The sensor(s) 213 may generate analog and/ordigital signals indications of which may be stored in the memory 211 andprocessed by the DSP 231 and/or the processor 230 in support of one ormore applications such as, for example, applications directed topositioning and/or navigation operations.

The sensor(s) 213 may be used in relative location measurements,relative location determination, motion determination, etc. Informationdetected by the sensor(s) 213 may be used for motion detection, relativedisplacement, dead reckoning, sensor-based location determination,and/or sensor-assisted location determination. The sensor(s) 213 may beuseful to determine whether the UE 200 is fixed (stationary) or mobileand/or whether to report certain useful information to the LMF 120regarding the mobility of the UE 200. For example, based on theinformation obtained/measured by the sensor(s) 213, the UE 200 maynotify/report to the LMF 120 that the UE 200 has detected movements orthat the UE 200 has moved, and report the relative displacement/distance(e.g., via dead reckoning, or sensor-based location determination, orsensor-assisted location determination enabled by the sensor(s) 213). Inanother example, for relative positioning information, the sensors/IMUcan be used to determine the angle and/or orientation of the otherdevice with respect to the UE 200, etc.

The IMU 270 may be configured to provide measurements about a directionof motion and/or a speed of motion of the UE 200, which may be used inrelative location determination. For example, the one or moreaccelerometers 273 and/or the one or more gyroscopes 274 of the IMU 270may detect, respectively, a linear acceleration and a speed of rotationof the UE 200. The linear acceleration and speed of rotationmeasurements of the UE 200 may be integrated over time to determine aninstantaneous direction of motion as well as a displacement of the UE200. The instantaneous direction of motion and the displacement may beintegrated to track a location of the UE 200. For example, a referencelocation of the UE 200 may be determined, e.g., using the SPS receiver217 (and/or by some other means) for a moment in time and measurementsfrom the accelerometer(s) 273 and gyroscope(s) 274 taken after thismoment in time may be used in dead reckoning to determine presentlocation of the UE 200 based on movement (direction and distance) of theUE 200 relative to the reference location.

The magnetometer(s) 271 may determine magnetic field strengths indifferent directions which may be used to determine orientation of theUE 200. For example, the orientation may be used to provide a digitalcompass for the UE 200. The magnetometer(s) 271 may include atwo-dimensional magnetometer configured to detect and provideindications of magnetic field strength in two orthogonal dimensions.Also or alternatively, the magnetometer(s) 271 may include athree-dimensional magnetometer configured to detect and provideindications of magnetic field strength in three orthogonal dimensions.The magnetometer(s) 271 may provide means for sensing a magnetic fieldand providing indications of the magnetic field, e.g., to the processor210.

The transceiver 215 may include a wireless transceiver 240 and a wiredtransceiver 250 configured to communicate with other devices throughwireless connections and wired connections, respectively. For example,the wireless transceiver 240 may include a transmitter 242 and receiver244 coupled to one or more antennas 246 for transmitting (e.g., on oneor more uplink channels and/or one or more sidelink channels) and/orreceiving (e.g., on one or more downlink channels and/or one or moresidelink channels) wireless signals 248 and transducing signals from thewireless signals 248 to wired (e.g., electrical and/or optical) signalsand from wired (e.g., electrical and/or optical) signals to the wirelesssignals 248. Thus, the transmitter 242 may include multiple transmittersthat may be discrete components or combined/integrated components,and/or the receiver 244 may include multiple receivers that may bediscrete components or combined/integrated components. The wirelesstransceiver 240 may be configured to communicate signals (e.g., withTRPs and/or one or more other devices) according to a variety of radioaccess technologies (RATs) such as 5G New Radio (NR), GSM (Global Systemfor Mobiles), UMTS (Universal Mobile Telecommunications System), AMPS(Advanced Mobile Phone System), CDMA (Code Division Multiple Access),WCDMA (Wideband CDMA), LTE (Long-Term Evolution), LTE Direct (LTE-D),3GPP LTE-V2X (PC5), IEEE 802.11 (including IEEE 802.11p), WiFi, WiFiDirect (WiFi-D), Bluetooth®, Zigbee etc. New Radio may use mm-wavefrequencies and/or sub-6 GHz frequencies. The wired transceiver 250 mayinclude a transmitter 252 and a receiver 254 configured for wiredcommunication, e.g., with the network 135 to send communications to, andreceive communications from, the gNB 110 a, for example. The transmitter252 may include multiple transmitters that may be discrete components orcombined/integrated components, and/or the receiver 254 may includemultiple receivers that may be discrete components orcombined/integrated components. The wired transceiver 250 may beconfigured, e.g., for optical communication and/or electricalcommunication. The transceiver 215 may be communicatively coupled to thetransceiver interface 214, e.g., by optical and/or electricalconnection. The transceiver interface 214 may be at least partiallyintegrated with the transceiver 215.

The user interface 216 may comprise one or more of several devices suchas, for example, a speaker, microphone, display device, vibrationdevice, keyboard, touch screen, etc. The user interface 216 may includemore than one of any of these devices. The user interface 216 may beconfigured to enable a user to interact with one or more applicationshosted by the UE 200. For example, the user interface 216 may storeindications of analog and/or digital signals in the memory 211 to beprocessed by DSP 231 and/or the general-purpose processor 230 inresponse to action from a user. Similarly, applications hosted on the UE200 may store indications of analog and/or digital signals in the memory211 to present an output signal to a user. The user interface 216 mayinclude an audio input/output (I/O) device comprising, for example, aspeaker, a microphone, digital-to-analog circuitry, analog-to-digitalcircuitry, an amplifier and/or gain control circuitry (including morethan one of any of these devices). Other configurations of an audio I/Odevice may be used. Also or alternatively, the user interface 216 maycomprise one or more touch sensors responsive to touching and/orpressure, e.g., on a keyboard and/or touch screen of the user interface216.

The SPS receiver 217 (e.g., a Global Positioning System (GPS) receiver)may be capable of receiving and acquiring SPS signals 260 via an SPSantenna 262. The antenna 262 is configured to transduce the wirelesssignals 260 to wired signals, e.g., electrical or optical signals, andmay be integrated with the antenna 246. The SPS receiver 217 may beconfigured to process, in whole or in part, the acquired SPS signals 260for estimating a location of the UE 200. For example, the SPS receiver217 may be configured to determine location of the UE 200 bytrilateration using the SPS signals 260. The general-purpose processor230, the memory 211, the DSP 231 and/or one or more specializedprocessors (not shown) may be utilized to process acquired SPS signals,in whole or in part, and/or to calculate an estimated location of the UE200, in conjunction with the SPS receiver 217. The memory 211 may storeindications (e.g., measurements) of the SPS signals 260 and/or othersignals (e.g., signals acquired from the wireless transceiver 240) foruse in performing positioning operations. The general-purpose processor230, the DSP 231, and/or one or more specialized processors, and/or thememory 211 may provide or support a location engine for use inprocessing measurements to estimate a location of the UE 200.

The UE 200 may include the camera 218 for capturing still or movingimagery. The camera 218 may comprise, for example, an imaging sensor(e.g., a charge coupled device or a CMOS imager), a lens,analog-to-digital circuitry, frame buffers, etc. Additional processing,conditioning, encoding, and/or compression of signals representingcaptured images may be performed by the general-purpose processor 230and/or the DSP 231. Also or alternatively, the video processor 233 mayperform conditioning, encoding, compression, and/or manipulation ofsignals representing captured images. The video processor 233 maydecode/decompress stored image data for presentation on a display device(not shown), e.g., of the user interface 216.

The position (motion) device (PMD) 219 may be configured to determine aposition and possibly motion of the UE 200. For example, the PMD 219 maycommunicate with, and/or include some or all of, the SPS receiver 217.The PMD 219 may also or alternatively be configured to determinelocation of the UE 200 using terrestrial-based signals (e.g., at leastsome of the signals 248) for trilateration, for assistance withobtaining and using the SPS signals 260, or both. The PMD 219 may beconfigured to use one or more other techniques (e.g., relying on theUE's self-reported location (e.g., part of the UE's position beacon))for determining the location of the UE 200, and may use a combination oftechniques (e.g., SPS and terrestrial positioning signals) to determinethe location of the UE 200. The PMD 219 may include one or more of thesensors 213 (e.g., gyroscope(s), accelerometer(s), magnetometer(s),etc.) that may sense orientation and/or motion of the UE 200 and provideindications thereof that the processor 210 (e.g., the processor 230and/or the DSP 231) may be configured to use to determine motion (e.g.,a velocity vector and/or an acceleration vector) of the UE 200. The PMD219 may be configured to provide indications of uncertainty and/or errorin the determined position and/or motion.

Referring also to FIG. 3 , an example of a TRP 300 of the BSs 110 a, 110b, 114 comprises a computing platform including a processor 310, memory311 including software (SW) 312, a transceiver 315, and (optionally) anSPS receiver 317. The processor 310, the memory 311, the transceiver315, and the SPS receiver 317 may be communicatively coupled to eachother by a bus 320 (which may be configured, e.g., for optical and/orelectrical communication). One or more of the shown apparatus (e.g., awireless interface and/or the SPS receiver 317) may be omitted from theTRP 300. The SPS receiver 317 may be configured similarly to the SPSreceiver 217 to be capable of receiving and acquiring SPS signals 360via an SPS antenna 362. The processor 310 may include one or moreintelligent hardware devices, e.g., a central processing unit (CPU), amicrocontroller, an application specific integrated circuit (ASIC), etc.The processor 310 may comprise multiple processors (e.g., including ageneral-purpose/application processor, a DSP, a modem processor, a videoprocessor, and/or a sensor processor as shown in FIG. 2 ). The memory311 is a non-transitory storage medium that may include random accessmemory (RAM)), flash memory, disc memory, and/or read-only memory (ROM),etc. The memory 311 stores the software 312 which may beprocessor-readable, processor-executable software code containinginstructions that are configured to, when executed, cause the processor310 to perform various functions described herein. Alternatively, thesoftware 312 may not be directly executable by the processor 310 but maybe configured to cause the processor 310, e.g., when compiled andexecuted, to perform the functions. The description may refer only tothe processor 310 performing a function, but this includes otherimplementations such as where the processor 310 executes software and/orfirmware. The description may refer to the processor 310 performing afunction as shorthand for one or more of the processors contained in theprocessor 310 performing the function. The description may refer to theTRP 300 performing a function as shorthand for one or more appropriatecomponents of the TRP 300 (and thus of one of the BSs 110 a, 110 b, 114)performing the function. The processor 310 may include a memory withstored instructions in addition to and/or instead of the memory 311.Functionality of the processor 310 is discussed more fully below.

The transceiver 315 may include a wireless transceiver 340 and a wiredtransceiver 350 configured to communicate with other devices throughwireless connections and wired connections, respectively. For example,the wireless transceiver 340 may include a transmitter 342 and receiver344 coupled to one or more antennas 346 for transmitting (e.g., on oneor more uplink channels) and/or receiving (e.g., on one or more downlinkchannels) wireless signals 348 and transducing signals from the wirelesssignals 348 to wired (e.g., electrical and/or optical) signals and fromwired (e.g., electrical and/or optical) signals to the wireless signals348. Thus, the transmitter 342 may include multiple transmitters thatmay be discrete components or combined/integrated components, and/or thereceiver 344 may include multiple receivers that may be discretecomponents or combined/integrated components. The wireless transceiver340 may be configured to communicate signals (e.g., with the UE 200, oneor more other UEs, and/or one or more other devices) according to avariety of radio access technologies (RATs) such as 5G New Radio (NR),GSM (Global System for Mobiles), UMTS (Universal MobileTelecommunications System), AMPS (Advanced Mobile Phone System), CDMA(Code Division Multiple Access), WCDMA (Wideband CDMA), LTE (Long-TermEvolution), LTE Direct (LTE-D), 3GPP LTE-V2X (PC5), IEEE 802.11(including IEEE 802.11p), WiFi, WiFi Direct (WiFi-D), Bluetooth®, Zigbeeetc. The wired transceiver 350 may include a transmitter 352 and areceiver 354 configured for wired communication, e.g., with the network140 to send communications to, and receive communications from, the LMF120, for example. The transmitter 352 may include multiple transmittersthat may be discrete components or combined/integrated components,and/or the receiver 354 may include multiple receivers that may bediscrete components or combined/integrated components. The wiredtransceiver 350 may be configured, e.g., for optical communicationand/or electrical communication.

The configuration of the TRP 300 shown in FIG. 3 is an example and notlimiting of the disclosure, including the claims, and otherconfigurations may be used. For example, the description hereindiscusses that the TRP 300 is configured to perform or performs severalfunctions, but one or more of these functions may be performed by aserver and/or the UE 200 (i.e., the LMF 120 and/or the UE 200 may beconfigured to perform one or more of these functions).

Referring also to FIG. 4 , an example of a server 400 comprises acomputing platform including a processor 410, memory 411 includingsoftware (SW) 412, and a transceiver 415. The processor 410, the memory411, and the transceiver 415 may be communicatively coupled to eachother by a bus 420 (which may be configured, e.g., for optical and/orelectrical communication). One or more of the shown apparatus (e.g., awireless interface) may be omitted from the server 400. The processor410 may include one or more intelligent hardware devices, e.g., acentral processing unit (CPU), a microcontroller, an applicationspecific integrated circuit (ASIC), etc. The processor 410 may comprisemultiple processors (e.g., including a general-purpose/applicationprocessor, a DSP, a modem processor, a video processor, and/or a sensorprocessor as shown in FIG. 2 ). The memory 411 is a non-transitorystorage medium that may include random access memory (RAM)), flashmemory, disc memory, and/or read-only memory (ROM), etc. The memory 411stores the software 412 which may be processor-readable,processor-executable software code containing instructions that areconfigured to, when executed, cause the processor 410 to perform variousfunctions described herein. Alternatively, the software 412 may not bedirectly executable by the processor 410 but may be configured to causethe processor 410, e.g., when compiled and executed, to perform thefunctions. The description may refer only to the processor 410performing a function, but this includes other implementations such aswhere the processor 410 executes software and/or firmware. Thedescription may refer to the processor 410 performing a function asshorthand for one or more of the processors contained in the processor410 performing the function. The description may refer to the server 400(or the LMF 120) performing a function as shorthand for one or moreappropriate components of the server 400 (e.g., the LMF 120) performingthe function. The processor 410 may include a memory with storedinstructions in addition to and/or instead of the memory 411.Functionality of the processor 410 is discussed more fully below.

The transceiver 415 may include a wireless transceiver 440 and a wiredtransceiver 450 configured to communicate with other devices throughwireless connections and wired connections, respectively. For example,the wireless transceiver 440 may include a transmitter 442 and receiver444 coupled to one or more antennas 446 for transmitting (e.g., on oneor more downlink channels) and/or receiving (e.g., on one or more uplinkchannels) wireless signals 448 and transducing signals from the wirelesssignals 448 to wired (e.g., electrical and/or optical) signals and fromwired (e.g., electrical and/or optical) signals to the wireless signals448. Thus, the transmitter 442 may include multiple transmitters thatmay be discrete components or combined/integrated components, and/or thereceiver 444 may include multiple receivers that may be discretecomponents or combined/integrated components. The wireless transceiver440 may be configured to communicate signals (e.g., with the UE 200, oneor more other UEs, and/or one or more other devices) according to avariety of radio access technologies (RATs) such as 5G New Radio (NR),GSM (Global System for Mobiles), UMTS (Universal MobileTelecommunications System), AMPS (Advanced Mobile Phone System), CDMA(Code Division Multiple Access), WCDMA (Wideband CDMA), LTE (Long-TermEvolution), LTE Direct (LTE-D), 3GPP LTE-V2X (PC5), IEEE 802.11(including IEEE 802.11p), WiFi, WiFi Direct (WiFi-D), Bluetooth®, Zigbeeetc. The wired transceiver 450 may include a transmitter 452 and areceiver 454 configured for wired communication, e.g., with the network135 to send communications to, and receive communications from, the TRP300, for example. The transmitter 452 may include multiple transmittersthat may be discrete components or combined/integrated components,and/or the receiver 454 may include multiple receivers that may bediscrete components or combined/integrated components. The wiredtransceiver 450 may be configured, e.g., for optical communicationand/or electrical communication.

The configuration of the server 400 shown in FIG. 4 is an example andnot limiting of the disclosure, including the claims, and otherconfigurations may be used. For example, the wireless transceiver 440may be omitted. Also or alternatively, the description herein discussesthat the server 400 is configured to perform or performs severalfunctions, but one or more of these functions may be performed by theTRP 300 and/or the UE 200 (i.e., the TRP 300 and/or the UE 200 may beconfigured to perform one or more of these functions).

One or more of many different techniques may be used to determineposition of an entity such as the UE 105. For example, knownposition-determination techniques include RTT, multi-RTT, OTDOA (alsocalled TDOA and including UL-TDOA and DL-TDOA), Enhanced CellIdentification (E-CID), DL-AoD, UL-AoA, etc. RTT uses a time for asignal to travel from one entity to another and back to determine arange between the two entities. The range, plus a known location of afirst one of the entities and an angle between the two entities (e.g.,an azimuth angle) can be used to determine a location of the second ofthe entities. In multi-RTT (also called multi-cell RTT), multiple rangesfrom one entity (e.g., a UE) to other entities (e.g., TRPs) and knownlocations of the other entities may be used to determine the location ofthe one entity. In TDOA techniques, the difference in travel timesbetween one entity and other entities may be used to determine relativeranges from the other entities and those, combined with known locationsof the other entities may be used to determine the location of the oneentity. Angles of arrival and/or departure may be used to help determinelocation of an entity. For example, an angle of arrival or an angle ofdeparture of a signal combined with a range between devices (determinedusing signal, e.g., a travel time of the signal, a received power of thesignal, etc.) and a known location of one of the devices may be used todetermine a location of the other device. The angle of arrival ordeparture may be an azimuth angle relative to a reference direction suchas true north. The angle of arrival or departure may be a zenith anglerelative to directly upward from an entity (i.e., relative to radiallyoutward from a center of Earth). E-CID uses the identity of a servingcell, the timing advance (i.e., the difference between receive andtransmit times at the UE), estimated timing and power of detectedneighbor cell signals, and possibly angle of arrival (e.g., of a signalat the UE from the base station or vice versa) to determine location ofthe UE. In TDOA, the difference in arrival times at a receiving deviceof signals from different sources along with known locations of thesources and known offset of transmission times from the sources are usedto determine the location of the receiving device.

Referring to FIG. 5 , with further reference to FIGS. 1-4 , a conceptualdiagram 500 of an example line of sight between a base station 502 and amobile device (e.g., the UE 105) is shown. The base station may be a TRP300 such as the eNB 110 a. The base station 502 may be configured withbeam forming technology to generate a plurality of transmit and/orreceive beams 504. The UE 105 may be a 5G NR mobile device with beamforming features and configured to generate a plurality of transmitand/or receive beams 105 a. In an example, the base station 502 and theUE 105 may be configured for full duplex operation such that therespective transceivers 340, 240 are configured to transmit and receivesimultaneously. The diagram 500 includes a simplified multipath scenariowhere the base station 502 and the UE 105 may communicate with oneanother via a LOS path 506 or one or more non-LOS (NLOS) paths such as afirst NLOS path 508 and a second NOS path 510. The LOS and NLOS paths506, 508, 510 may be based on one or more transmit beams generated bythe base station 502 and the UE 105. For example, a wide transmit beamtransmitted by the base station 502 may reach the UE 105 via the LOSpath 506 as well as via one or more of the NLOS paths 508, 510. Whilethe NLOS paths 508, 510 may be adequate for communication, theadditional distance traveled between the base station 502 and the UE 105may reduce the accuracy of the distance/position estimate for the UE105. Weak LOS paths may also impact the accuracy of the positiondistance estimate.

Referring to FIG. 6 , with further reference to FIG. 5 , a conceptualdiagram of an example position determination based on a line of sightsignal is shown. LOS delay estimation is the first step in positioningfor several methods such as ToA, TDoA and RTT based methods. Forexample, the LOS delay associated with the LOS path 506 may be used todetermine a radius of a circle 602 around the base station 502. Theposition of the UE 105 along the circumference of the circle 602 may bebased on uplink (UL) angle of arrival (AoA) measured by the base station502. The NN based estimator described herein provides improved LOS delayestimation for a variety of weak LOS and multipath scenarios as comparedto conventional algorithms such as Matrix Pencil delay estimation andThreshold peak detection and interpolation.

In an OFDM system with a subcarrier spacing Δf and K subcarriers, thesystem bandwidth (BW) is then B=KΔf. The channel frequency response(CFR) between two nodes such as the base station 502 and the UE 105 maybe expressed as:

$\begin{matrix}{H_{k} = {{\sum_{\,{m = 0}}^{\,{M - 1}}{\alpha_{m}e^{{- j}2\pi k\Delta f\tau_{m}}}} + w_{k}}} & (1)\end{matrix}$

where,

-   -   Δf=subcarrier spacing;    -   k=number of subcarriers;    -   M=number of channel paths;    -   (σ_(m), τ_(m)), m=0, 1, . . . , M−1=the path gains and delays of        the channel from the transmitter to the receiver; and    -   H_(k)=CFR (i.e., channel gain) on the k^(th) subcarrier.

The constant w_(k) is modeled is an added white Gaussian noise (AWGN)with variance E[|w_(k)|²]=σ². The average channel power is normalizedsuch that Σ_(m=0) ^(M-1)E[|α_(m)|]²=1, and signal to noise ratio isdefined as SNR=1/σ². The objective in LOS estimation is to determine thevalue of τ0, the delay of the first arriving path in the channel impulseresponse (CIR). Historically, accurate estimation of τ0 has beenchallenging in weak LOS path and multipath scenarios.

Referring to FIG. 7 , a flow diagram of an example process 700 forgenerating a channel impulse response input for a neural network isshown. The process 700 receives the CFR (i.e., Hi, as described above)at stage 702. The CIR output of the process 700 is composed of complexnumbers including a real part 704 a and an imaginary part 704 b. In anexample, the magnitude of the CIR may be utilized and may improve theoverall performance of the NN. An oversampling process may be used tosmooth the band-limited impulse response (e.g., 1× to 4×) and improvethe delay estimation. The oversampled CIR may be generated byzero-padding the CFR to the right length at stage 706 and thenperforming a large point Inverse Fast-Fourier Transform (IFFT) at stage708. In an embodiment, the process may optionally perform one or moreshift, scaling and truncation operations to reduce the NN inputcomplexity. In general, in realistic channels, a large fraction of theenergy in the CIR is contained within a few time-domain samples. Thismay be used to reduce the input complexity by shifting the CIR at stage710 and/or truncating the oversampled CIR to capture most of the inputenergy of the channel. For example, if the LOS delay is very close tozero, a part of the CIR peak is wrapped around due to the IFFT operationat stage 708. In this example, the CIR is artificially delayed by a fewsamples to enable the LOS path to be captured within the truncationwindow at stage 712. The input features to the NN may also be scaled atstage 714 such that the peak of the CIR magnitude is unity. Thepreprocessing may be used to homogenize the CIR from various physicalscenarios to enable processing with a single NN.

In an example, if the LOS path is very weak and the next significantarriving path has a large delay compared to the LOS path, the CIRtruncation procedure may miss the LOS path. In this case, additionalsamples in the CIR truncation window may be utilized to reduce theprobability of missing a weak LOS path.

The disclosed NN may be configured to individually process the CIR fromeach transmit-receive antenna pair and the output delay from all theantenna pairs may be combined in postprocessing. A motivation behindthis choice is that spatial correlation among the antennas is a strongfunction of the devices' antenna layout and that such information maynot be readily available to the devices in a commercial network (e.g.,assuming a uniform linear or planar array is not a realistic option,especially in small cells). Also, if the positioning signals aretransmitted from a mixture of macro and small cells, they would havedifferent antenna configurations. Single Input Single Output (SISO)processing allows the trained NN to be reused across a wider range ofantenna architectures.

Referring to FIG. 8 , with further reference to FIG. 7 , a block diagramof an example neural network (NN) 800 for determining a line of sightdelay estimate 816 is shown. The CIR input 802 (i.e., the real andimaginary parts 704 a-b in FIG. 7 ) are used as the input to the NN 800.In general, a delayed input CIR 802 should result in an equivalentlydelayed value of the estimated LOS path delay 816. A plurality of 1Dconvolutional layers 804, 806, 808, 810 may be used to capture the delaytranslation property between the input and output (i.e., a translationequivariance in delay domain). In an example, the CFR may be useddirectly as an input to the NN 800. In this example, each path delay maycorrespond to a linear phase ramp in the frequency domain and the CFR isthe weighted sum of all such linear phase ramps. The path with theunwrapped phase slope of the lowest magnitude corresponds to the firstarrival path. Extracting this information from processing the frequencydomain coefficients may require additional processing capabilities. Theexample discussed herein utilizes the CIR computed in FIG. 7 as an inputto the NN 800.

In an example, the architecture for the NN 800 exploits the translationequivariance between the input and the desired output. The NN 800includes four convolutional layers 804, 806, 808, 810, followed by twofully connected layers 812, 814. Referring to FIGS. 9A and 9B, theconvolutional layers 804, 806, 808, 810, may utilize one or more of apointwise convolution layer 900 and/or a depth wise convolution layer910. The pointwise convolution layer 900 is configured to combine acrosschannels, and the depth wise convolution layer 910 is configured tocombine within a channel In an example, a depth wise separableconvolutional layer is used rather than a fully convolutional layer foreach of the input convolutional layers. The use of separableconvolutional layers may reduce the complexity and the number of weightssignificantly in the NN 800 without significantly degrading performance.A standard leaky rectified linear unit (ReLU) with leakage factor fornegative input values may be used as the non-linearity for all layersexcept the last fully connected layer 814. In an example, max-poolingand batch-normalization may be used after the convolutional layers.

In an example, each train/test data point may be generated using a4-step procedure including sampling from dataset parameters, generatinga power delay profile (PDP), generating channel gain and delay, andgenerate a CFR. The hyper-parameters of the channel are first generatedfrom Table 1.

TABLE 1 Param. Name DataSet A DataSet B Distribution LOS Delay (ns) 0-128 0-40 Uniform Num. Paths 2-15 2-7  Uniform Delay Spread (ns) 0-128 0-48 Uniform Rician Factor (linear) 0.05-2    0.05-2    UniformPer path power decay (dB) 2-4  2-4  Uniform SNR (dB) 5-30 5-30 Uniform

For example, for dataset A, an LOS delay is uniformly chosen between [0,128] ns, the number of channel paths uniformly from {2, 3, . . . , 15}and so on. Using the channel hyper-parameters in the previous step, achannel PDP may be generated and normalized such that sum power of allthe paths in the PDP including the LOS path is unity. The LOS path maybe assigned a uniform phase between [0, 2π] and a complex Gaussiannumber with the specified power is drawn for each NLOS path in the PDPas its path gain. A corresponding delay is assigned to each path. Thechannel gains and delays are then combined with the scenario descriptionin Table 2 to generate the CFR and is stored as one sample in thedatabase. The delay of the first arriving path is stored as the groundtruth measurement for training the network.

TABLE 2 SCS BW # SCs OFDM FFT OS Sample T_(S) CIR Scenario (kHz) (MHz)(K) Size Factor (ns) Window Dataset 1 30 100 3276 4096 4 2.03 256 A 2 30200 3276 * 2 4096 * 2 2 2.03 256 A 3 30 400 3276 * 4 4096 * 4 1 2.03 256A 4 60 400 3276 * 2 4096 * 2 1 2.03 256 A 5 120 400 3276 4096 1 2.03 256A 6 30 400 3276 * 4 4096 * 4 4 0.507 256 B 7 30 Equal mixture of samplesfrom scenarios 1, 2 and 3.

The NN weights may be trained independently for each scenario in Table 2using an Adam optimizer. For each scenario, an adaptive learning rateschedule may be used. For example, the schedule may start with 10-3 andthen drop to 10-4 and 10-5 at 25 and 50 epochs respectively. Trainingmay be observed to converge at this learning rate and the entire networkmay be trained for 60 epochs, where each epoch runs through all thetraining examples in batches of 50. The average training and test lossis recorded per epoch and may translated to distance error incentimeters to enable an easy comparison across scenarios.

While the NN 800 includes the CIR as an input, the NN 800 may be trainedbased on other inputs. For example, the magnitude of the impulseresponse (i.e., abs(CIR)), an angle of the CIR and transformation of theangle (e.g., sin, cos) may be used as inputs. Other features may also beused such as logarithmic functions (e.g., log(abs(CIR)), scale factorafter normalization, signal-to-noise (SNR) estimates of the CFR or CIR.Other signal related parameters may also be used in the NN 800. The NN800 may also be augmented with pooling and batch normalization layersbased on complexity/performance requirements. Some connections may beskipped in large networks. In an example, the output of the NN 800 mayinclude a quality estimate to indicate confidence in the output. Thequality estimate may be based on a variance or a standard deviation ofthe delay. Other features of the NN 800 may be modified to impactperformance and accuracy parameters. For example, weights may betruncated (e.g., bit length or otherwise) to reduce complexity and matchdesired accuracy. A network may indicate a desired accuracy to the UEand the UE may be configured to adapt the NN weights accordingly.

While the output 816 in FIG. 8 indicates a LOS delay estimate, the NN800 architecture may be adapted for other inputs such as angle ofarrival (AoA) and/or angle of departure (AoD) estimates.

Referring to FIG. 10 , with further reference to FIGS. 1-8 , examplemessage flows 1000 between a base station 502 and a mobile device (e.g.,UE 105) are shown. The message flows 1000 may be based on existing ormodified communication protocols such as NPP or NRPP. In an example themessages may be included in other protocol specifications such as radioresource control (RRC). In an example, a base station 502 may beconfigured to send a UE capability message 1002 to ascertain thecapabilities to provide an LOS delay estimate based on a neural network.The UE 105 may be configured to respond with NN network information(e.g., based on configuration) and/or Angle of Arrival capabilities in aNN report message 1004. In an example, the base station 502 may initiatea positioning session with the UE 105 by transmitting a positioninginitiation message 1006. The UE 105 may be configured to respond with aconfiguration response message 1008 including configuration informationassociated with a NN to use. For example, the configuration informationmay include the UE antenna configuration, phase state PDP, or otherinformation the base station may use to select a NN to use forestimating the LOS delay. In response to receiving the configurationresponse message 1008, the base station 502 may determine a NN to useand provide the corresponding NN information (e.g., model/algorithm) tothe UE 105 in one or more NN information messages 1010. In an example,the UE 105 may be configured with a plurality of local NN informationand the base station 502 may provide an index to indicate which local NNinformation to use in the NN information messages 1010.

In an example, the base station 502 may provide one or more messages(e.g., RRC, SIB, MAC) to configure semi-periodic or periodic SoundingReference Signals (SRS) in a SRS configuration message 1012. The UE 105may be configured to transmit SRS 1014 to enable the base station 502 toestimate PDP and UL AoA characteristics. The SRS may be precoded withdownlink (DL) channel information. In response to receiving the SRS1014, the base station 502 may determine a NN to use and provide thecorresponding NN information (e.g., model/algorithm or local index) tothe UE 105 in one or more NN information messages 1016.

Referring to FIG. 11 , with further reference to FIGS. 1-10 , examplemessage flows 1100 between a base station 502 and a mobile device (e.g.,the UE 105) for retraining a neural network (NN) are shown. The NN 800may include a plurality of nodes and weighted inputs to the nodes.Reference signals such as PRS and SRS provide information about thecombined effect of multipath fading, scattering, doppler and powerlosses in transmitted signals. The information given by the PRS and SRSsignals may be used for online retraining of NN weights in the NN 800.For example, the base station 502 may generate and transmit PRS signals1102. The UE 105 may be configured to update NN weights in the local NNinformation using the ground truth of the previously stored conventionalalgorithm. Similarly, the UE 105 may generate and transmit SRS signals1104. The base station 502, or other associated network servers such asthe LMF 120, may be configured to update NN weights using the groundtruth from the previously stored conventional algorithm. The retrainingprocedure may be used to adapt the NN 800 to an environment, RF filterson the base station 502 and/or the UE 105, or other RF distortions thatmay impact the performance of the NN 800.

Referring to FIG. 12 , with further reference to FIGS. 1-11 , an exampledata structure 1200 for neural network models is shown. The datastructure 1200 may include one or more data bases 1202 including one ormore data tables such as a device table 1204, an algorithm table 1206and a base station table 1208. The data structure 1200 may includerelational database applications (e.g., Oracle, SQL, dBase, etc.), flatfiles (e.g., JSON, XML, CVS), binary files, or other file structuresconfigured to persist and index neural network models. The datastructure 1200 may include other instructions such as stored proceduresconfigured to query, update, append and index the tables in the database 1202. In general, neural network information may includearchitectures, weights and bias matrices associated with the neurons ina neural network. In an example, the neural network information maypersist in a data structure such that an executing neural network mayutilize the data structure to process an input. In an example, theneural network may comprise a executable file (e.g., compiled) whichincludes the architectures, weights and bias matrices. The device table1204 may include fields associated with a mobile device and the channelstate which may be used to select an appropriate neural network. Forexample, the device table 1204 may include a UEID field to identify aparticular UE and/or a UEmodelID field to identify the product and modelinformation of the UE. The UEmodelID field may be associated withantenna configurations and other form factors which can be associatedwith a NN. The antenna configuration may indicate a layout and a phasecoherence state of the antennas (e.g., fully, partially, non-coherent).A UEestPos field may identify the current or historical estimatedpositions of the UE. The estimated location of the UE may be used toselect a NN to use for LOS delay estimation. For example, different NNsmay be used for different environments and specific locations (e.g.,indoor, urban, factory floor, mall, office, etc.). An OperatingFreqfield may be used to indicate the frequencies and/or channel informationthe UE is configured to operate on. Other fields may also be used tocharacterize the channel state and/or the configuration of a UE. One ormore of the fields in the device table 1204 may be used to select NNfiles from the algorithm table 1206. In an example, the records in thealgorithm table 1206 may include architecture, weight, bias matrices,etc. for NNs trained based on the parameters stored in the device table1204 and/or the base station table 1208. In an example, the algorithmtable 1206 may include binary files (e.g., compiled programs) whichinclude a complete NN capable of receiving a CIR input and outputting aLOS delay estimate as depicted in FIG. 8 . The base station table 1208may include fields associated with the operational parameters of a basestation. For example, a BSID field may indicate a unique ID of a basestation, a BSLoc field may indicate the location of the base station,and a BSconfig field may indicate configuration details of the basestation such as antenna configurations and orientations. For example,the configuration information may include other fields associated with acell such as the number of antennas, phase coherence, spatial structure,operating frequency, antenna spacing, layout, used set of elements, etc.These fields are examples only and not limitations as other fields maybe used to categorize base stations and associated cells. The records inthe base station table 1208 may be associated with one or more NNrecords in the algorithm table 1206.

The fields and tables described in the data structure 1200 are examplesonly. The data structure may be constructed to enable a device, such asa network server (e.g. the server 400, LMF 120, etc.) or a UE toassociate configuration and operational parameters with one or moreneural networks. Thus, a neural network may be selected based on theconfiguration of the UE, the configuration of a base station, or othercombinations of network resources and operational parameters (e.g.,proximity of neighbors, time of day, device density, network traffic,etc.). In an example, a positioning method (e.g., RTT, ToA, TDoA, etc.)may be used to select a neural network.

Referring to FIG. 13A, with further reference to FIGS. 1-12 , a method1300 of providing neural network information to a mobile device includesthe stages shown. The method 1300 is, however, an example only and notlimiting. The method 1300 may be altered, e.g., by having stages added,removed, rearranged, combined, performed concurrently, and/or havingsingle stages split into multiple stages.

At stage 1302, the method includes determining receiver configurationinformation or channel state information for a mobile device. A server400 and the transceiver 415, or a UE 200 may be a means for determiningreceiver configuration or channel state. In an example, referring toFIG. 10 , a base station 502 may initiate a positioning session and theUE 105 may be configured to provide configuration response messages 1008including the receiver configuration information. The receiveconfiguration information may also be transceiver configurationinformation based on the capabilities of a device (i.e., a transceivermay include a combination of receiver and transmitter components). In anexample, the UE 200 may include local configuration information storedin the memory 211 and/or detectable by the processor 230. In an example,the receiver configuration may indicate antenna configuration (e.g.,layout) and the phase coherence state of antennas. For example, fullycoherent (i.e., phase aligned combining at multiple receive antennas),partially coherent (i.e., subsets of antenna elements are phasealigned), or non-coherent (i.e., no phase alignment across antennaelements). The receiver configuration information may define a state ofthe UE and may include physical and electrical configuration andassociated channel state information such as antenna configuration,phase state, PDP, or other variables that are associated with channelstate and the receive chain in the UE 200.

At stage 1304, the method includes determining neural networkinformation based on the receiver configuration information or thechannel state information. A server 400 or a UE 200 may be a means fordetermining neural network information. Referring to FIG. 12 , a server400 and/or a UE 200 may include one or more elements of the datastructure 1200. The receiver configuration information determined atstage 1302 may correspond to one or more records in the device table1204. The data base 1202 may be queried based on the receiverconfiguration or channel state to determine one or more records in thealgorithm table 1206. The records in the algorithm table 1206 mayinclude neural network data such as architecture, weight, bias matrices,etc. associated with distributed neural network algorithms. In anexample, the neural network information may be adjusted based on a PDPreceived from the UE. The neural network information may be a indexnumber associated with a list of neural networks stored locally on theUE. The UE 200 may be configured to determine the neural networkinformation locally without assistance from the network. In an example,the server 400 or the UE 200 may be configured to utilize SNR or otherenvironmental inputs to dynamically determine whether to use the NN 800or conventional algorithms to compute the LOS delay estimate. Forexample, conventional algorithms may be a more efficient option in lowSNR (e.g., clear view) environments, and a neural network solution maybe used for high SNR environments.

At stage 1306, the method includes providing the neural networkinformation to the mobile device. The server 400 and the transceiver415, or a UE 200 may be a means for providing the neural networkinformation. The base station 502 may be configured to send a NNinformation message 1010 including the neural network informationdetermined at stage 1304. In an example, the NN information message 1010may include neural network data such as architecture, weight, biasmatrices, etc. In an example, the neural network message may include anexecutable file or an index associated with neural network informationstored locally on the UE. In an example, providing the neural networkinformation may include providing the information locally from thememory 211 to the processor 230 within the UE 200.

The method 1300 may include one or more of the following features. Thereceiver configuration information may include an antenna configuration,and the antenna configuration may include a phase coherence state of theantenna configuration. The channel state information may include a powerdelay profile. The receiver configuration information may include anoperating frequency and bandwidth. The neural network information may bestored on a network server and/or on the mobile device. The neuralnetwork information may include an architecture of the neural networkand its weight and bias matrices. The weight and bias values may betruncated to reduce a complexity of the neural network information.

Referring to FIG. 13B, with further reference to FIGS. 1-12 , a method1320 of computing a line of sight delay based on neural networkinformation includes the stages shown. The method 1320 is, however, anexample only and not limiting. The method 1320 may be altered, e.g., byhaving stages added, removed, rearranged, combined, performedconcurrently, and/or having single stages split into multiple stages.

At stage 1322, the method includes receiving reference signalinformation. The transceiver 340 or the transceiver 240 may be means forreceiving reference signal information. In an example, a TRP 300 mayreceive SRS signals from the UE 200. In another example, the UE 200 mayreceive PRS signals from the TRP 300. Other reference signals such asCSI-RS and DMRS may be used as reference signals which may be receivedby either the TRP 300 or the UE 200.

At stage 1324, the method includes determining one or more channelcharacteristics based on the reference signal information. The processor310 or the processor 230 may be a means for determining the one or morechannel characteristics. The reference signal information may be used todetermine information about the combined effect of multipath fading,scattering, doppler and power losses in transmitted signals. In anexample, the UE 200 or the TRP 300 may determine a PDP value and achannel frequency response based on the reference signal information.The channel frequency response may be computed based on equation (1).

At stage 1326, the method includes determining neural networkinformation based on the one or more channel characteristics. The TRP300 and the UE 200 may be means for determining neural networkinformation. In an example, the TRP 300 may receive the SRS signals fromthe UE 200 and determine the PDP based on the SRS signals. The PDP valuemay be used to select neural network information from the data structure1200. Other configuration information may be used to select the neuralnetwork information. In an example, the UE 200 may be configured todetermine a PDP based on the PRS signals received from the TRP 300. TheUE 200 may provide the PDP information, and other configurationinformation, to the TRP 300 via a configuration response message 1008,and the TRP may determine the neural network information. The UE 200 maydetermine the neural network information locally based in part on thePDP value.

At stage 1328, the method includes computing a line of sight delayestimate based at least in part on the neural network information. Theprocessor 310 or the processor 230 may be a means for computing the lineof sight delay. The TRP 300 or the UE 200 may determine CIR values basedon the CFR determined at stage 1324. For example, the process 700 may beused to determine the CIR values. The CIR values may be input into theNN 800 to determine the line of sight delay estimate. The architecture,weight and bias matrices in the NN 800 are based on the neural networkinformation determined at stage 1326. In an example, the NN 800 may bemodified based on the PDP. The output of the NN 800 may include aquality estimate indicating a confidence in the delay estimate.

Referring to FIG. 14 , with further reference to FIGS. 1-12 , a method1400 for determining a line of sight delay includes the stages shown.The method 1400 is, however, an example only and not limiting. Themethod 1400 may be altered, e.g., by having stages added, removed,rearranged, combined, performed concurrently, and/or having singlestages split into multiple stages.

At stage 1402, the method includes receiving reference signalinformation. The transceiver 340 or the transceiver 240 may be means forreceiving reference signal information. In an example, a TRP 300 mayreceive SRS signals from the UE 200. In another example, the UE 200 mayreceive PRS signals from the TRP 300. Other reference signals such asCSI-RS and DMRS may be used as reference signals which may be receivedby either the TRP 300 or the UE 200.

At stage 1404, the method includes determining a channel frequencyresponse or a channel impulse response based on the reference signalinformation. The processor 310 or the processor 230 may be a means fordetermining the channel frequency response (CFR) or the channel impulseresponse (CIR). The CFR between the TRP 300 and the UE 200 may be basedon equation (1) described above. Referring to FIG. 7 , determining theCIR includes determining a real part 704 a of the CIR and an imaginarypart 704 b of the CIR.

At stage 1406, the method includes processing the channel frequencyresponse or the channel impulse response with a neural network. Theprocessor 310 or the processor 230 may be a means for processing theCIR. The neural network may be based on configuration informationassociated with the TRP 300 and/or the UE 200. For example, referring toFIG. 12 , the data structure 1200 includes a plurality of neuralnetworks based on the respective configurations of the TRP 300 and theUE 200. The neural network may be based on antenna configurations,including layout and phase coherence states of the antennas. Otherphysical, electrical and environmental parameters may be used to selecta neural network.

At stage 1408, the method includes determining a line of sight delay, anangle of arrival, or an angle of departure value based on an output ofthe neural network. The processor 310 or the processor 230 may be ameans for determining the output of the NN. The output 816 is based onthe architecture and training of the NN 800. In an example, the output816 may include a line of sight delay estimate. In other examples, theoutput 816 may include an angle of arrival estimate or an angle ofdeparture estimate. The output 816 may also include a quality estimate.The quality estimate may be based on a variance or a standard deviationof the output 816. If the quality estimate is above a determinedthreshold, the weights of the NN 800 may be modified (e.g., based a PDPvalue), or another neural network from the data structure may beselected. A satisfactory line of sight delay estimate may be used inmethods for positioning the UE 200 including RTT, ToA, and TDoA.

Other examples and implementations are within the scope of thedisclosure and appended claims. For example, due to the nature ofsoftware and computers, functions described above can be implementedusing software executed by a processor, hardware, firmware, hardwiring,or a combination of any of these. Features implementing functions mayalso be physically located at various positions, including beingdistributed such that portions of functions are implemented at differentphysical locations. For example, one or more functions, or one or moreportions thereof, discussed above as occurring in the server 400 may beperformed outside of the server 400 such as by the TRP 300.

As used herein, the singular forms “a,” “an,” and “the” include theplural forms as well, unless the context clearly indicates otherwise.For example, “a processor” may include one processor or multipleprocessors. The terms “comprises,” “comprising,” “includes,” and/or“including,” as used herein, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

Also, as used herein, “or” as used in a list of items prefaced by “atleast one of” or prefaced by “one or more of” indicates a disjunctivelist such that, for example, a list of “at least one of A, B, or C,” ora list of “one or more of A, B, or C” means A or B or C or AB or AC orBC or ABC (i.e., A and B and C), or combinations with more than onefeature (e.g., AA, AAB, ABBC, etc.).

Substantial variations may be made in accordance with specificrequirements. For example, customized hardware might also be used,and/or particular elements might be implemented in hardware, software(including portable software, such as applets, etc.) executed by aprocessor, or both. Further, connection to other computing devices suchas network input/output devices may be employed.

The systems and devices discussed above are examples. Variousconfigurations may omit, substitute, or add various procedures orcomponents as appropriate. For instance, features described with respectto certain configurations may be combined in various otherconfigurations. Different aspects and elements of the configurations maybe combined in a similar manner. Also, technology evolves and, thus,many of the elements are examples and do not limit the scope of thedisclosure or claims.

A wireless communication system is one in which communications areconveyed wirelessly, i.e., by electromagnetic and/or acoustic wavespropagating through atmospheric space rather than through a wire orother physical connection. A wireless communication network may not haveall communications transmitted wirelessly, but is configured to have atleast some communications transmitted wirelessly. Further, the term“wireless communication device,” or similar term, does not require thatthe functionality of the device is exclusively, or evenly primarily, forcommunication, or that the device be a mobile device, but indicates thatthe device includes wireless communication capability (one-way ortwo-way), e.g., includes at least one radio (each radio being part of atransmitter, receiver, or transceiver) for wireless communication.

Specific details are given in the description to provide a thoroughunderstanding of example configurations (including implementations).However, configurations may be practiced without these specific details.For example, well-known circuits, processes, algorithms, structures, andtechniques have been shown without unnecessary detail in order to avoidobscuring the configurations. This description provides exampleconfigurations only, and does not limit the scope, applicability, orconfigurations of the claims. Rather, the preceding description of theconfigurations provides a description for implementing describedtechniques. Various changes may be made in the function and arrangementof elements without departing from the scope of the disclosure.

The terms “processor-readable medium,” “machine-readable medium,” and“computer-readable medium,” as used herein, refer to any medium thatparticipates in providing data that causes a machine to operate in aspecific fashion. Using a computing platform, various processor-readablemedia might be involved in providing instructions/code to processor(s)for execution and/or might be used to store and/or carry suchinstructions/code (e.g., as signals). In many implementations, aprocessor-readable medium is a physical and/or tangible storage medium.Such a medium may take many forms, including but not limited to,non-volatile media and volatile media. Non-volatile media include, forexample, optical and/or magnetic disks. Volatile media include, withoutlimitation, dynamic memory.

A statement that a value exceeds (or is more than or above) a firstthreshold value is equivalent to a statement that the value meets orexceeds a second threshold value that is slightly greater than the firstthreshold value, e.g., the second threshold value being one value higherthan the first threshold value in the resolution of a computing system.A statement that a value is less than (or is within or below) a firstthreshold value is equivalent to a statement that the value is less thanor equal to a second threshold value that is slightly lower than thefirst threshold value, e.g., the second threshold value being one valuelower than the first threshold value in the resolution of a computingsystem.

Implementation examples are described in the following numbered clauses:

1. A method for determining a line of sight delay, an angle of arrival,or an angle of departure value, comprising:

-   -   receiving reference signal information;    -   determining a channel frequency response or a channel impulse        response based on the reference signal information;    -   processing the channel frequency response or the channel impulse        response with a neural network; and    -   determining the line of sight delay, the angle of arrival, or        the angle of departure value based on an output of the neural        network.

2. The method of clause 1 wherein the reference signal information is asounding reference signal.

3. The method of clause 1 wherein the reference signal information is apositioning reference signal.

4. The method of clause 1 wherein the reference signal information is achannel state information reference signal.

5. The method of clause 1 further comprising determining the neuralnetwork based at least in part of a positioning method used fordetermining a location of a mobile device.

6. The method of clause 5 further comprising determining the neuralnetwork based at least in part on a receiver configuration.

7. The method of clause 6 wherein the receiver configuration includes anantenna configuration and a phase coherence state of the antennaconfiguration.

8. The method of clause 5 wherein the determining the neural networkincludes transmitting neural network information from a network to themobile device.

9. The method of clause 5 wherein determining the neural networkincludes transmitting an indication of a selected neural network from alist of neural networks available at the mobile device.

10. The method of clause 1 wherein the neural network is one of aplurality of neural networks stored in a data structure.

11. The method of clause 1 further comprising determining a requireddesired accuracy associated with the output of the neural network, andwherein processing the channel impulse response with the neural networkincludes adapting one or more weights in the neural network based on therequired desired accuracy.

12. The method of clause 1 wherein the output of the neural networkincludes a quality estimate.

13. The method of clause 12 wherein determining the line of sight delay,the angle of arrival, or the angle of departure value is based at leastin part on the quality estimate.

14. An apparatus for determining a line of sight delay, an angle ofarrival, or an angle of departure value, comprising:

-   -   a memory;    -   at least one transceiver;    -   at least one processor communicatively coupled to the memory and        the at least one transceiver, and configured to:    -   receive reference signal information;    -   determine a channel frequency response or a channel impulse        response based on the reference signal information;    -   process the channel frequency response or the channel impulse        response with a neural network; and    -   determine the line of sight delay, the angle of arrival, or the        angle of departure value based on an output of the neural        network.

15. The apparatus of clause 14 wherein the reference signal informationis a sounding reference signal.

16. The apparatus of clause 14 wherein the reference signal informationis a positioning reference signal.

17. The apparatus of clause 14 wherein the reference signal informationis a channel state information reference signal.

18. The apparatus of clause 14 wherein the at least one processor isfurther configured to determine the neural network based at least inpart on a positioning method used to determine a location of a mobiledevice.

19. The apparatus of clause 18 wherein the at least one processor isfurther configured to determine the neural network based at least inpart on a receiver configuration.

20. The apparatus of clause 19 wherein the receiver configurationincludes an antenna configuration and a phase coherence state of theantenna configuration.

21. The apparatus of clause 14 wherein the at least one processor isfurther configured to transmit neural network information from a networkto a mobile device.

22. The apparatus of clause 14 wherein the at least one processor isfurther configured to transmit an indication of a selected neuralnetwork from a list of neural networks available at a mobile device.

23. The apparatus of clause 14 wherein the neural network is one of aplurality of neural networks stored in a data structure.

24. The apparatus of clause 14 wherein the at least one processor isfurther configured to determine a required desired accuracy associatedwith the line of sight delay and adapt one or more weights in the neuralnetwork based on the required desired accuracy.

25. The apparatus of clause 14 wherein the output of the neural networkincludes a quality estimate.

26. The apparatus of clause 25 wherein the at least one processor isfurther configured to determine the line of sight delay, the angle ofarrival, or the angle of departure value based at least in part on thequality estimate.

27. An apparatus for determining a line of sight delay, an angle ofarrival, or an angle of departure value, comprising:

-   -   means for receiving reference signal information;    -   means for determining a channel frequency response or a channel        impulse response based on the reference signal information;    -   means for processing the channel frequency response or the        channel impulse response with a neural network; and    -   means for determining the line of sight delay, the angle of        arrival, or the angle of departure value based on an output of        the neural network.

28. The apparatus of clause 27 wherein the reference signal informationis associated with at least one of a sounding reference signal, apositioning reference signal, or a channel state information referencesignal.

29. A non-transitory processor-readable storage medium comprisingprocessor-readable instructions configured to cause one or moreprocessors to determine a line of sight delay, an angle of arrival, oran angle of departure value, comprising:

-   -   code for receiving reference signal information;    -   code for determining a channel frequency response or a channel        impulse response based on the reference signal information;    -   code for processing the channel frequency response or the        channel impulse response with a neural network; and    -   code for determining the line of sight delay, the angle of        arrival, or the angle of departure value based on an output of        the neural network.

30. The non-transitory processor-readable storage medium of clause 29wherein the determining the neural network includes at least one oftransmitting neural network information from a network to a mobiledevice, or transmitting an indication of a selected neural network froma list of neural networks available at the mobile device.

31. A method, performed on a mobile device, for determining a line ofsight delay, an angle of arrival, or an angle of departure value,comprising:

-   -   receiving reference signal information;    -   determining a channel frequency response or a channel impulse        response based on the reference signal information;    -   processing the channel frequency response or the channel impulse        response with a neural network; and    -   determining the line of sight delay, the angle of arrival, or        the angle of departure value based on an output of the neural        network.

32. The method of clause 31 wherein the reference signal information isa positioning reference signal measurement.

33. The method of clause 31 further comprising determining the neuralnetwork based at least in part on a receiver configuration in the mobiledevice.

34. The method of clause 33 wherein the determining the neural networkincludes receiving neural network information from a network server.

35. The method of clause 33 wherein determining the neural networkincludes receiving an indication of a selected neural network from alist of neural networks available at the mobile device.

36. The method of clause 31 wherein the neural network is one of aplurality of neural networks stored in a data structure on the mobiledevice.

37. An apparatus for determining a line of sight delay, an angle ofarrival, or an angle of departure value, comprising:

-   -   a memory;    -   at least one transceiver;    -   at least one processor communicatively coupled to the memory and        the at least one transceiver, and configured to:    -   receive reference signal information;    -   determine a channel frequency response or a channel impulse        response based on the reference signal information;    -   process the channel frequency response or the channel impulse        response with a neural network; and    -   determine the line of sight delay, the angle of arrival, or the        angle of departure value based on an output of the neural        network.

38. The apparatus of clause 37 wherein the reference signal informationis a positioning reference signal measurement.

39. The apparatus of clause 37 wherein the at least one processor isfurther configured to determine the neural network based at least inpart on a positioning method used to determine a location of theapparatus.

40. The apparatus of clause 37 wherein the neural network is one of aplurality of neural networks stored in a data structure in the memory.

The invention claimed is:
 1. A method for determining a line of sightdelay, an angle of arrival, or an angle of departure value, comprising:receiving reference signal information; determining a channel frequencyresponse or a channel impulse response based on the reference signalinformation; processing the channel frequency response or the channelimpulse response with a neural network; and determining the line ofsight delay, the angle of arrival, or the angle of departure value basedon an output of the neural network, wherein the output of the neuralnetwork is based at least on the channel frequency response or thechannel impulse response input into the neural network.
 2. The method ofclaim 1 wherein the reference signal information is a sounding referencesignal measurement.
 3. The method of claim 1 wherein the referencesignal information is a channel state information reference signalmeasurement.
 4. The method of claim 1 further comprising determining theneural network based at least in part of a positioning method used fordetermining a location of a mobile device.
 5. The method of claim 4further comprising determining the neural network based at least in parton a receiver configuration.
 6. The method of claim 5 wherein thereceiver configuration includes an antenna configuration and a phasecoherence state of the antenna configuration.
 7. The method of claim 1wherein the neural network is one of a plurality of neural networksstored in a data structure.
 8. The method of claim 1 further comprisingdetermining a required desired accuracy associated with the output ofthe neural network, and wherein processing the channel impulse responsewith the neural network includes adapting one or more weights in theneural network based on the required desired accuracy.
 9. The method ofclaim 1 wherein the output of the neural network includes a qualityestimate.
 10. The method of claim 9 wherein determining the line ofsight delay, the angle of arrival, or the angle of departure value isbased at least in part on the quality estimate.
 11. An apparatus fordetermining a line of sight delay, an angle of arrival, or an angle ofdeparture value, comprising: a memory; at least one transceiver; atleast one processor communicatively coupled to the memory and the atleast one transceiver, and configured to: receive reference signalinformation; determine a channel frequency response or a channel impulseresponse based on the reference signal information; process the channelfrequency response or the channel impulse response with a neuralnetwork; and determine the line of sight delay, the angle of arrival, orthe angle of departure value based on an output of the neural network,wherein the output of the neural network is based at least on thechannel frequency response or the channel impulse response input intothe neural network.
 12. The apparatus of claim 11 wherein the referencesignal information is a sounding reference signal measurement.
 13. Theapparatus of claim 11 wherein the reference signal information is achannel state information reference signal measurement.
 14. Theapparatus of claim 11 wherein the at least one processor is furtherconfigured to determine the neural network based at least in part on apositioning method used to determine a location of a mobile device. 15.The apparatus of claim 14 wherein the at least one processor is furtherconfigured to determine the neural network based at least in part on aconfiguration of the at least one transceiver.
 16. The apparatus ofclaim 15 wherein the configuration of the at least one transceiverincludes an antenna configuration and a phase coherence state of theantenna configuration.
 17. The apparatus of claim 11 wherein the neuralnetwork is one of a plurality of neural networks stored in a datastructure.
 18. The apparatus of claim 11 wherein the at least oneprocessor is further configured to determine a required desired accuracyassociated with the line of sight delay and adapt one or more weights inthe neural network based on the required desired accuracy.
 19. Theapparatus of claim 11 wherein the output of the neural network includesa quality estimate.
 20. The apparatus of claim 19 wherein the at leastone processor is further configured to determine the line of sightdelay, the angle of arrival, or the angle of departure value based atleast in part on the quality estimate.
 21. A method, performed on amobile device, for determining a line of sight delay, an angle ofarrival, or an angle of departure value, comprising: receiving referencesignal information; determining a channel frequency response or achannel impulse response based on the reference signal information;processing the channel frequency response or the channel impulseresponse with a neural network; and determining the line of sight delay,the angle of arrival, or the angle of departure value based on an outputof the neural network, wherein the output of the neural network is basedat least on the channel frequency response or the channel impulseresponse input into the neural network.
 22. The method of claim 21wherein the reference signal information is a positioning referencesignal measurement.
 23. The method of claim 21 further comprisingdetermining the neural network based at least in part on a receiverconfiguration in the mobile device.
 24. The method of claim 23 whereinthe determining the neural network includes receiving neural networkinformation from a network server.
 25. The method of claim 23 whereindetermining the neural network includes receiving an indication of aselected neural network from a list of neural networks available at themobile device.
 26. The method of claim 21 wherein the neural network isone of a plurality of neural networks stored in a data structure on themobile device.